Ovis-Image Technical Report
Guo-Hua Wang, Liangfu Cao, Tianyu Cui, Minghao Fu, Xiaohao Chen, Pengxin Zhan, Jianshan Zhao, Lan Li, Bowen Fu, Jiaqi Liu, Qing-Guo Chen
2025-12-03
Summary
This paper introduces Ovis-Image, a new artificial intelligence model that creates images from text descriptions, with a special focus on making the text *within* those images look really good.
What's the problem?
Existing AI models that generate images from text often struggle to accurately and clearly render text within the image itself. The best models are also very large and require a lot of computing power, making them difficult for many people to use. Essentially, it's hard to get AI to write legible signs or captions in pictures, and the tools that *can* do it well are often inaccessible.
What's the solution?
The researchers built Ovis-Image by combining a powerful 'brain' for understanding both text and images (called Ovis 2.5) with a part specifically designed to create the visual image. They then trained this model using a lot of text and images, focusing specifically on how to make text look good. The key is they didn't need a *huge* model to get good results, meaning it can run on a single, powerful computer graphics card.
Why it matters?
This work is important because it shows you can create high-quality images with clear text without needing massive amounts of computing resources or relying on proprietary, closed-source AI. This makes advanced image generation more accessible and opens the door for wider use in applications like creating educational materials, marketing content, or even just fun art projects.
Abstract
We introduce Ovis-Image, a 7B text-to-image model specifically optimized for high-quality text rendering, designed to operate efficiently under stringent computational constraints. Built upon our previous Ovis-U1 framework, Ovis-Image integrates a diffusion-based visual decoder with the stronger Ovis 2.5 multimodal backbone, leveraging a text-centric training pipeline that combines large-scale pre-training with carefully tailored post-training refinements. Despite its compact architecture, Ovis-Image achieves text rendering performance on par with significantly larger open models such as Qwen-Image and approaches closed-source systems like Seedream and GPT4o. Crucially, the model remains deployable on a single high-end GPU with moderate memory, narrowing the gap between frontier-level text rendering and practical deployment. Our results indicate that combining a strong multimodal backbone with a carefully designed, text-focused training recipe is sufficient to achieve reliable bilingual text rendering without resorting to oversized or proprietary models.